Encoding of variability of landmark-based spatial information.

Recent evidence suggests humans optimally weight visual and haptic information (i.e., in inverse proportion to their variances). A more recent proposal is that spatial information (i.e., distance and direction) may also adhere to Bayesian principles and be weighted in an optimal fashion. A fundamental assumption of this proposal is that participants encode the variability of spatial information. In a three-dimensional virtual-environment open-field search task, we provide evidence that participants encoded the variability of landmark-based spatial information. Specifically, participants searched for a hidden goal location in a 5 × 5 matrix of raised bins. Participants experienced five training phases in which they searched for a hidden goal that maintained a unique spatial relationship to each of four distinct landmarks. Each landmark was assigned an a priori value of locational uncertainty such that each varied in its ability to predict a goal (i.e., varied in number of potential goal locations). Following training, participants experienced conflict trials in which two distinct landmarks were presented simultaneously. Participants preferentially responded to the landmark with the lower uncertainty value (i.e., smaller number of potential goal locations). Results provide empirical evidence for the encoding of variability of landmark-based spatial information and have implications for theoretical accounts of spatial learning.